Discriminating Against New Classes: One-class versus Multi-class Classification

Hempstalk, Kathryn; Frank, Eibe

doi:10.1007/978-3-540-89378-3_32

Cited by 30 publications

(20 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(8). Weighted average was used in order to prevent target classes with smaller instance counts from adversely affecting the results (Hempstalk & Frank, 2008). Friedman's rank test is a nonparametric test analogous to a standard one-way repeatedmeasures analysis of variance (Howell, 2013).…”

Section: Number Of Categories Categoriesmentioning

confidence: 99%

Fuzzy based binary feature profiling for modus operandi analysis

Chamikara

Galappaththi

Yapa

et al. 2016

PeerJ Computer Science

View full text Add to dashboard Cite

It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level.

show abstract

Section: Number Of Categories Categoriesmentioning

confidence: 99%

Fuzzy based binary feature profiling for modus operandi analysis

Chamikara

Galappaththi

Yapa

et al. 2016

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…It has been successfully applied in many novelty detection and classification tasks, including communication network performance [3], wireless sensor networks [4], forensic science [5], detection of handwritten digits [6] and objet recognition [7], only to name a few. Moreover, it has been extended naturally to binary and multiclass classification tasks, by applying a single one-class classifier to each class and subsequently combining the decision rules [8].…”

Section: Introductionmentioning

confidence: 99%

On simple one-class classification methods

Noumir

Honeiné

Richard

2012

2012 IEEE International Symposium on Information Theory Proceedings

View full text Add to dashboard Cite

The one-class classification has been successfully applied in many communication, signal processing, and machine learning tasks. This problem, as defined by the oneclass SVM approach, consists in identifying a sphere enclosing all (or the most) of the data. The classical strategy to solve the problem considers a simultaneous estimation of both the center and the radius of the sphere. In this paper, we study the impact of separating the estimation problem. It turns out that simple one-class classification methods can be easily derived, by considering a least-squares formulation. The proposed framework allows us to derive some theoretical results, such as an upper bound on the probability of false detection. The relevance of this work is illustrated on well-known datasets.

show abstract

“…The AUC is used in this work for comparisons because it is independent of any threshold used by the learning algorithm. It is not influenced by decision biases and prior probabilities, and it places the performance of diverse systems on a common, easily interpreted scale [14].…”

Section: Evaluation Using the Auc Scorementioning

confidence: 99%

“…To compare classifier performance on the entire multi-class transactional dataset, we use the weighted average AUC, where each target class c i is weighted according to its prevalence [14]:…”

Section: Evaluation Using the Auc Scorementioning

confidence: 99%

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Apeh

Žliobaitė

Pechenizkiy

et al. 2012

Research and Development in Intelligent Systems XXIX

View full text Add to dashboard Cite

Predicting the class of a customer profile is a key task in marketing, which enables businesses to approach the right customer with the right product at the right time through the right channel to satisfy the customer's evolving needs. However, due to costs, privacy and/or data protection, only the business' owned transactional data is typically available for constructing customer profiles. Predicting the class of customer profiles based on such data is challenging, as the data tends to be very large, heavily sparse and highly skewed. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data from the food sales domain consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved.

show abstract

Discriminating Against New Classes: One-class versus Multi-class Classification

Cited by 30 publications

References 2 publications

Fuzzy based binary feature profiling for modus operandi analysis

Fuzzy based binary feature profiling for modus operandi analysis

On simple one-class classification methods

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Contact Info

Product

Resources

About